schemap 0.2.0

Automatic Pydantic Schemas for SQLAlchemy models

Schemap

Automatic Pydantic v2 schemas from SQLAlchemy 2.0 ORM models: no more manually mirroring your database columns in Pydantic.

Note: This 0.2.0 release provides the core schema generation functionality. Schema configuration and customization are actively being worked on.

pip install schemap

Quick Start

Define your models with AutoBase. Schemas are generated automatically:

from schemap import AutoBase
from sqlalchemy.orm import Mapped, mapped_column

class User(AutoBase):
    __tablename__ = "users"

    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str]
    email: Mapped[str]

# Four schemas are available as class attributes:
User.Schema           # all columns
User.CreateSchema     # excludes id, defaults (for inserts)
User.UpdateSchema     # all fields optional (for partial updates)
User.PublicSchema     # excludes fields starting with "__"

# Round-trip between ORM and Pydantic:
data = User.CreateSchema(name="Alice", email="[e-mail]")
user = User.from_schema(data)
schema = user.to_schema()

Standalone Usage

Use build_schema without AutoBase:

from schemap import build_schema
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column

class Base(DeclarativeBase):
    pass

class User(Base):
    __tablename__ = "users"
    id: Mapped[int] = mapped_column(primary_key=True)
    name: Mapped[str]

UserSchema = build_schema(User, "default")

Schema Variants

Variant	Filtering	Nullability
"default" (.Schema)	all columns	matches model
"create" (.CreateSchema)	excludes PKs, server_defaults, defaults	matches model
"update" (.UpdateSchema)	excludes PKs	all fields Optional[type] = None
"public" (.PublicSchema)	excludes __-prefixed columns	matches model

Requirements

Python ≥ 3.12.7, SQLAlchemy ≥ 2.0.49, Pydantic ≥ 2.13.4.

License

MIT